​Optimized Deep Q-learning for Automated Atari Space Invaders: An Implementation in Tensorflow 2.0.

🔗 Optimized Deep Q-learning for Automated Atari Space Invaders: An Implementation in Tensorflow 2.0.
Exploring the Importance of Data Preprocessing

​The future of Machine Learning

🔗 The future of Machine Learning
A look into the future of ML with Jeff Dean

​Why is Python Programming a perfect fit for Big Data?

🔗 Why is Python Programming a perfect fit for Big Data?
We’ll discuss in the blog the major benefits of using Python for big data.

🎥 Machine Learning: A New Approach to Drug Discovery with Daphne Koller - #332
👁 1 раз ⏳ 2621 сек.
Today we continue our 2019 NeurIPS coverage joined by Daphne Koller, co-Founder and former co-CEO of Coursera and Founder and CEO of Insitro. We caught up with Daphne to discuss:

Her background in machine learning, beginning in ‘93, and her work with the Stanford online machine learning courses, and eventually her work at Coursera. The current landscape of pharmaceutical drug discovery, including the current pricing of drugs and misnomers with why drugs are so expensive, Her work at Insitro, a compan

​One of the best Machine Learning Professors
Full series on ML by CalTech Prof. Yaser Abu-Mostafa
https://www.youtube.com/watch?v=idu8kaPFf1A&list=PL41qI9AD63BMXtmes0upOcPA5psKqVkgS

🔗 CalTech ML Course Lecture 01 - The Learning Problem
The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommo

🎥 Decision Tree in Machine Learning | Great Learning Live Session
👁 1 раз ⏳ 4920 сек.
In this live session, we will take your through the concepts of decision tree machine learning algorithm and demonstrate in Python.

#DecisionTree #MachineLearning #GreatLearning
Agenda:
- Decision Tree Concepts
- Demo R/Python
- Finding Impurity of a Node
-- Entropy
-- Gini Index

- Great Learning has collaborated with the University of Texas at Austin for the PG Program in Artificial Intelligence and Machine Learning and with UT Austin McCombs School of Business for the PG Program in Analytics and Bu

​Top 7 Modern programming languages to learn now

🔗 Top 7 Modern programming languages to learn now
How Rust, Go, Kotlin, TypeScript, Swift, Dart, Julia can boost your career and improve your software development skills

​Introducing NVIDIA DRIVE AGX Orin: Vehicle Performance for the AI Era

https://blogs.nvidia.com/blog/2019/12/17/ai-baidu-alibaba-accelerate/

🔗 As AI Universe Keeps Expanding, NVIDIA CEO Lays Out Plan to Accelerate All of It | The Official NVID
With the AI revolution spreading across industries everywhere, NVIDIA founder and CEO Jensen Huang took the stage Wednesday to unveil the latest technology for speeding its mass adoption. His talk — to more than 6,000 scientists, engineers and entrepreneurs gathered for this week’s GPU Technology Conference in Suzhou, two hours west of Shanghai — touched Read article ?

🎥 How to make a neural network with tensorflow
👁 1 раз ⏳ 1161 сек.
How to quickly and easily make your first neural network. No setup or software install required. This code walkthrough uses Tensorflow and Keras layers.

There is lots more to learn like different loss functions, different activation functions, network architectures that belong in different videos.

You can jump right into the colab notebook here https://colab.research.google.com/drive/1eburtci3CUZrw-_Z-VhbNlrDxzrnFXFv

​Black Hole Machine Learning

🔗 Black Hole Machine Learning
Neural Networks vie Theoretical Physics

​Самые интересные применения машинного обучения в социальных сетях, маркетинге и другом в 2019 году.

https://www.geeksforgeeks.org/top-machine-learning-applications-in-2019/

🔗 Top Machine Learning Applications in 2019 - GeeksforGeeks
Suppose you want to search Machine Learning on Google. Well, the results you will see are carefully curated and ranked by Google using Machine Learning!!!… Read More »

🎥 Fields in Data Science | What are the different fields in data science?
👁 1 раз ⏳ 1140 сек.
In this video, you will understand the #Data #Science #Fields such as Mathematics, statistics, Machine Learning, Cluster Analysis, Data Mining, Big data Analytics, Data Visualization, Artificial Intelligence, Neural Networks, Deep Learning, Deep Active Learning, Cognitive Computing.

Get Data Science Training: https://www.besanttechnologies.com/training-courses/data-warehousing-training/datascience-training-institute-in-chennai

For Best Training and Certifications Contact Us Now!
📞 Classroom : +91 8099 770

​Advanced Google Skills for Data Science

🔗 Advanced Google Skills for Data Science
Optimize your programming by searching like a professional

​Multiple Linear Regression-Beginner’s Guide

🔗 Multiple Linear Regression-Beginner’s Guide
In this article i will be focusing on making a multiple linear regression model from scratch in python for beginners.

🎥 Fall 2019 Robotics Colloquium: Debadeepta Dey (Microsoft Research)
👁 1 раз ⏳ 3380 сек.
Lecture title: Imitation-Learning with Indirect Oracles

We present Vision-based Navigation with Language-based Assistance (VNLA), a grounded vision-language task where an agent with visual perception is guided via language to find objects in photorealistic indoor environments. The task emulates a real-world scenario in that (a) the requester may not know how to navigate to the target objects and thus makes requests by only specifying high-level endgoals, and (b) the agent is capable of sensing when it is l

🎥 The Future of Artificial Intelligence: Crash Course AI #20
👁 1 раз ⏳ 660 сек.
Today, in our final episode of Crash Course AI, we're going to look towards the future. We've spent much of this series explaining how and why we don't have the Artificial General Intelligence (or AGI) that we see in the movies like Bladerunner, Her, or Ex Machina. Siri frequently doesn't understand us, we probably shouldn't sleep in our self-driving cars, and those recommended videos on YouTube and Netflix often aren't what we really want to watch next. So let's talk about what we do know, how we got here,

​What is Going on Inside the Brain When We Listen to Music? - New World : Artificial Intelligence

🔗 What is Going on Inside the Brain When We Listen to Music? - New World : Artificial Intelligence
When you listen to music, multiple areas of your brain become engaged and active. But when you actually play an instrument, that activity becomes more like a full-body brain workout. What's going on?

​Streamlit dashboard to run SQL queries on BigQuery.

Blog post: https://imadelhanafi.com/posts/bigquery_dashboard/

Live version: https://bigquery.imadelhanafi.com

Github repo: https://github.com/imadelh/Bigquery-Streamlit

🔗 BigQuery dashboard with Streamlit :: Imad El Hanafi — Portfolio & Blog
Introduction Live version: https://bigquery.imadelhanafi.com Github repo: https://github.com/imadelh/Bigquery-Streamlit Storing and querying large datasets is an important step for data analysis and predictive modeling. BigQuery is a serverless data warehouse that allows storing data (up to Terabytes) and runs fast SQL queries without worrying about the computing power. In this post, we will discover how to interact with BigQuery and render results in an interactive dashboard built using Streamlit.

​Analyzing and Improving the Image Quality of StyleGAN

https://github.com/NVlabs/stylegan2

Paper : https://arxiv.org/abs/1912.04958v1

https://paperswithcode.com/paper/analyzing-and-improving-the-image-quality-of

🔗 NVlabs/stylegan2
StyleGAN2 - Official TensorFlow Implementation. Contribute to NVlabs/stylegan2 development by creating an account on GitHub.

​Measuring Dataset Granularity.

http://arxiv.org/abs/1912.10154

🔗 Measuring Dataset Granularity
Despite the increasing visibility of fine-grained recognition in our field, "fine-grained'' has thus far lacked a precise definition. In this work, building upon clustering theory, we pursue a framework for measuring dataset granularity. We argue that dataset granularity should depend not only on the data samples and their labels, but also on the distance function we choose. We propose an axiomatic framework to capture desired properties for a dataset granularity measure and provide examples of measures that satisfy these properties. We assess each measure via experiments on datasets with hierarchical labels of varying granularity. When measuring granularity in commonly used datasets with our measure, we find that certain datasets that are widely considered fine-grained in fact contain subsets of considerable size that are substantially more coarse-grained than datasets generally regarded as coarse-grained. We also investigate the interplay between dataset granularity with a variety of factors an